deniseransome2/oksei
1
0
Code Issues Actions Packages Projects Wiki

Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Denise Ransome 2025-05-31 02:48:23 +08:00
commit 5b2a8fb1fb
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>Today, we are excited to announce that DeepSeek R1 distilled Llama and Qwen [designs](http://carvis.kr) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.apps.calegix.net)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion [criteria](https://www.hyxjzh.cn13000) to build, experiment, and responsibly scale your generative [AI](https://git.pawott.de) ideas on AWS.<br>
<br>In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://60.23.29.213:3060) that uses support finding out to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its reinforcement learning (RL) step, which was utilized to fine-tune the [design's responses](https://gogs.fytlun.com) beyond the standard pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down intricate queries and reason through them in a detailed way. This guided thinking procedure enables the model to produce more precise, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MathewCutlack) transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while [concentrating](http://www.zhihutech.com) on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as agents, sensible thinking and data interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing effective inference by routing queries to the most relevant specialist "clusters." This approach allows the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to [release](http://www.kotlinx.com3000) the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 [distilled](https://unitenplay.ca) models bring the [reasoning abilities](https://hafrikplay.com) of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher model.<br>
<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://tobang-bangsu.co.kr) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit boost, produce a limit boost request and connect to your account team.<br>
<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and assess designs against crucial safety requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
<br>The [basic circulation](http://140.143.208.1273000) involves the following actions: First, the system gets an input for the model. This input is then [processed](http://sites-git.zx-tech.net) through the ApplyGuardrail API. If the input passes the [guardrail](https://hitechjobs.me) check, it's sent to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon [Bedrock Marketplace](https://demo.shoudyhosting.com) offers you access to over 100 popular, emerging, and [specialized structure](https://www.punajuaj.com) designs (FMs) through Amazon Bedrock. To [gain access](https://raumlaborlaw.com) to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
<br>1. On the Amazon Bedrock console, [select Model](https://nusalancer.netnation.my.id) brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.<br>
<br>The design detail page provides essential details about the model's capabilities, pricing structure, and implementation standards. You can discover detailed use instructions, consisting of sample API calls and [code snippets](http://117.71.100.2223000) for integration. The model supports numerous text generation jobs, [including](https://navar.live) content creation, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities.
The page likewise consists of release options and licensing details to help you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.<br>
<br>You will be [prompted](http://cwscience.co.kr) to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, go into a variety of instances (in between 1-100).
6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a [GPU-based instance](http://115.29.48.483000) type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you may desire to examine these settings to line up with your organization's security and compliance requirements.
7. [Choose Deploy](https://kenyansocial.com) to begin using the model.<br>
<br>When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive user interface where you can try out various triggers and adjust model specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, content for inference.<br>
<br>This is an outstanding way to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your triggers for optimal outcomes.<br>
<br>You can quickly test the model in the play area through the UI. However, to [conjure](http://huaang6688.gnway.cc3000) up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the [deployed](https://nextcode.store) DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to create text based on a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial [intelligence](http://dchain-d.com3000) (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can [release](http://43.139.182.871111) with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses two hassle-free approaches: using the user-friendly SageMaker [JumpStart UI](https://inicknet.com) or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that [finest suits](https://foxchats.com) your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The model browser displays available designs, with details like the name and design capabilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 [design card](https://www.laciotatentreprendre.fr).
Each design card reveals essential details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to [conjure](http://tigg.1212321.com) up the model<br>
<br>5. Choose the model card to see the design details page.<br>
<br>The model details page includes the following details:<br>
<br>- The design name and supplier details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of [crucial](http://208.167.242.1503000) details, such as:<br>
<br>- Model description.
- License details.
- Technical [specifications](https://git.xaviermaso.com).
- Usage standards<br>
<br>Before you deploy the design, it's [suggested](https://bgzashtita.es) to evaluate the design details and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to proceed with release.<br>
<br>7. For Endpoint name, utilize the automatically generated name or produce a custom one.
8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the variety of instances (default: 1).
Selecting proper instance types and counts is important for expense and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.<br>
<br>The deployment process can take several minutes to finish.<br>
<br>When release is total, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime client and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run extra demands against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also use the [ApplyGuardrail API](https://www.blatech.co.uk) with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, complete the actions in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
2. In the Managed deployments area, find the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the right deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [Amazon SageMaker](https://teba.timbaktuu.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://coastalplainplants.org) companies develop innovative options utilizing AWS services and [accelerated calculate](http://111.231.76.912095). Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of big language designs. In his leisure time, Vivek enjoys treking, viewing films, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://wiki.lexserve.co.ke) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://cn.wejob.info) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>[Jonathan Evans](https://www.jobcheckinn.com) is an [Expert Solutions](https://virnal.com) Architect dealing with generative [AI](http://secdc.org.cn) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://platform.giftedsoulsent.com) hub. She is passionate about building options that assist consumers accelerate their [AI](https://evertonfcfansclub.com) journey and unlock company value.<br>